ABSTRACT High-throughput experiments (HTEs) have been powerful tools to obtain many materials data. However, HTEs often require expensive equipment. Although high-throughput ab-initio calculation (HTC) has the potential to make materials big data easier to collect, HTC does not represent the actual materials data obtained by HTEs in many cases. Here we propose using a combination of simple HTEs, HTC, and machine learning to predict material properties. We demonstrate that our method enables accurate and rapid prediction of the Kerr rotation mapping of an FexCoyNi1-x-y composition spread alloy. Our method has the potential to quickly predict the properties of many materials without a difficult and expensive HTE and thereby accelerate materials development.

中文翻译：

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通过整合高通量实验、高通量 ab-initio 计算和机器学习来预测材料特性

摘要 高通量实验 (HTE) 已成为获取许多材料数据的有力工具。然而，HTE 通常需要昂贵的设备。尽管高通量 ab-initio 计算 (HTC) 有可能使材料大数据更容易收集，但在许多情况下，HTC 并不代表 HTE 获得的实际材料数据。在这里，我们建议结合使用简单的 HTE、HTC 和机器学习来预测材料特性。我们证明了我们的方法能够准确快速地预测 FexCoyNi1-xy 成分扩散合金的克尔旋转映射。我们的方法有可能在没有困难和昂贵的 HTE 的情况下快速预测许多材料的特性，从而加速材料开发。